from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 prediction (58.33%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	e70d8e7886766d41f30889506baa1e67	2cf49829391aa7891e877f2ed070adf0	1.955136	0.108803	NaN	0.000409	0.001955	brute	-1	1	0.663	0.200872	0.009173	0.687	9.733246	9.743389	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	22d5eac4a4146149b35eae6914921514	2cf49829391aa7891e877f2ed070adf0	2.860174	0.070873	NaN	0.000280	0.002860	brute	-1	5	0.757	0.203123	0.005553	0.742	14.081008	14.086268	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	2.213675	0.038159	NaN	0.000361	0.002214	brute	1	100	0.882	0.243158	0.005866	0.875	9.103871	9.106520	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	0.020864	0.002022	NaN	0.000038	0.020864	brute	1	100	1.000	0.009268	0.003457	0.000	2.251128	2.402638	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	2.993470	0.054501	NaN	0.000267	0.002993	brute	-1	100	0.882	0.243019	0.013504	0.875	12.317837	12.336838	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	0.025731	0.002804	NaN	0.000031	0.025731	brute	-1	100	1.000	0.008499	0.000842	0.000	3.027632	3.042442	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	efac583623711d395ddae7a1e881ff68	2cf49829391aa7891e877f2ed070adf0	2.280282	0.030067	NaN	0.000351	0.002280	brute	1	5	0.757	0.194381	0.006699	0.742	11.730964	11.737929	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	b79d63bb7670c492de5c3befac58fe29	2cf49829391aa7891e877f2ed070adf0	1.184285	0.008049	NaN	0.000676	0.001184	brute	1	1	0.663	0.189998	0.003723	0.687	6.233156	6.234352	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	e70d8e7886766d41f30889506baa1e67	88843f54689e3271092f70126e1de585	1.733147	0.027726	NaN	0.000009	0.001733	brute	-1	1	0.896	0.030211	0.002243	0.967	57.368840	57.526685	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	22d5eac4a4146149b35eae6914921514	88843f54689e3271092f70126e1de585	2.847563	0.058336	NaN	0.000006	0.002848	brute	-1	5	0.922	0.031710	0.000728	0.974	89.799672	89.823318	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	16f94fc93942ff7ece860c9b0f64645f	88843f54689e3271092f70126e1de585	2.135111	0.014854	NaN	0.000007	0.002135	brute	1	100	0.929	0.072381	0.003462	0.975	29.498210	29.531942	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2d5f2b2c02d77766434d9e5033b4a76c	88843f54689e3271092f70126e1de585	2.811007	0.039034	NaN	0.000006	0.002811	brute	-1	100	0.929	0.069442	0.001590	0.975	40.479764	40.490368	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	efac583623711d395ddae7a1e881ff68	88843f54689e3271092f70126e1de585	2.061697	0.025369	NaN	0.000008	0.002062	brute	1	5	0.922	0.029766	0.000977	0.974	69.262619	69.299887	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	b79d63bb7670c492de5c3befac58fe29	88843f54689e3271092f70126e1de585	1.085737	0.010496	NaN	0.000015	0.001086	brute	1	1	0.896	0.030876	0.001029	0.967	35.164530	35.184059	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.241	0.0	-1	1	0.051	0.004	0.216	0.217	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.210	0.0	-1	5	0.049	0.001	0.227	0.227	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.699	0.0	1	100	0.048	0.001	0.248	0.248	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.016	0.0	-1	100	0.050	0.003	0.230	0.230	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.765	0.0	1	5	0.048	0.001	0.249	0.249	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.885	0.0	1	1	0.048	0.001	0.240	0.240	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.353	0.0	-1	1	0.010	0.000	0.477	0.477	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.001	0.323	0.0	-1	5	0.008	0.000	0.587	0.588	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.329	0.0	1	100	0.009	0.000	0.534	0.534	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.316	0.0	-1	100	0.009	0.000	0.567	0.567	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.308	0.0	1	5	0.009	0.001	0.591	0.592	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.336	0.0	1	1	0.009	0.000	0.518	0.519	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.955	0.109	0.000	0.002	-1	1	0.201	0.009	9.733	9.743	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.003	0.000	0.022	-1	1	0.009	0.002	2.441	2.530	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.860	0.071	0.000	0.003	-1	5	0.203	0.006	14.081	14.086	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.003	0.000	0.024	-1	5	0.009	0.003	2.581	2.690	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.214	0.038	0.000	0.002	1	100	0.243	0.006	9.104	9.107	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.002	0.000	0.021	1	100	0.009	0.003	2.251	2.403	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.993	0.055	0.000	0.003	-1	100	0.243	0.014	12.318	12.337	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.003	0.000	0.026	-1	100	0.008	0.001	3.028	3.042	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.280	0.030	0.000	0.002	1	5	0.194	0.007	11.731	11.738	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	0.000	0.021	1	5	0.008	0.000	2.638	2.640	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.184	0.008	0.001	0.001	1	1	0.190	0.004	6.233	6.234	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.001	0.000	0.019	1	1	0.008	0.000	2.445	2.448	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.733	0.028	0.000	0.002	-1	1	0.030	0.002	57.369	57.527	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.004	0.000	0.005	-1	1	0.001	0.000	7.129	7.202	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.848	0.058	0.000	0.003	-1	5	0.032	0.001	89.800	89.823	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	0.000	0.006	-1	5	0.001	0.000	7.132	7.283	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.135	0.015	0.000	0.002	1	100	0.072	0.003	29.498	29.532	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	4.276	4.304	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.811	0.039	0.000	0.003	-1	100	0.069	0.002	40.480	40.490	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.010	0.002	0.000	0.010	-1	100	0.001	0.000	11.488	11.556	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.062	0.025	0.000	0.002	1	5	0.030	0.001	69.263	69.300	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.808	4.831	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.086	0.010	0.000	0.001	1	1	0.031	0.001	35.165	35.184	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.400	2.433	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	fe8267e25dadb302ed498e50fabf7ef4	1eb6cf1a720a225efb91df0b529b0510	0.850513	1.064081	NaN	0.000094	0.000851	kd_tree	-1	1	0.929	0.102109	0.003228	0.910	8.329477	8.333637	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	dc74b969bf622bc24ba3bc62c980983b	1eb6cf1a720a225efb91df0b529b0510	1.079550	0.386281	NaN	0.000074	0.001080	kd_tree	-1	5	0.946	0.202691	0.012861	0.941	5.326089	5.336799	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	41accfcdcd5c50784bedf6164b63de99	1eb6cf1a720a225efb91df0b529b0510	5.637007	0.623787	NaN	0.000014	0.005637	kd_tree	1	100	0.951	0.575086	0.011178	0.940	9.802020	9.803871	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	022f7445d43bb1dbc24dc3106c03cb93	1eb6cf1a720a225efb91df0b529b0510	3.259656	0.234377	NaN	0.000025	0.003260	kd_tree	-1	100	0.951	0.560219	0.003594	0.940	5.818540	5.818659	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	84622ba45e941db642965553529e1941	1eb6cf1a720a225efb91df0b529b0510	1.595688	0.171253	NaN	0.000050	0.001596	kd_tree	1	5	0.946	0.191443	0.004462	0.941	8.335060	8.337323	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	a9455565db1d8e052a783317c99744ff	1eb6cf1a720a225efb91df0b529b0510	0.939030	0.361407	NaN	0.000085	0.000939	kd_tree	1	1	0.929	0.105018	0.002140	0.910	8.941601	8.943458	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	fe8267e25dadb302ed498e50fabf7ef4	edf7cb014c2c0910e1c3aaaf8ae633f2	0.029046	0.015580	NaN	0.000551	0.000029	kd_tree	-1	1	0.891	0.000500	0.000137	0.879	58.057241	60.198328	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	dc74b969bf622bc24ba3bc62c980983b	edf7cb014c2c0910e1c3aaaf8ae633f2	0.024962	0.001064	NaN	0.000641	0.000025	kd_tree	-1	5	0.911	0.000742	0.000063	0.905	33.651294	33.772438	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	41accfcdcd5c50784bedf6164b63de99	edf7cb014c2c0910e1c3aaaf8ae633f2	0.039243	0.011174	NaN	0.000408	0.000039	kd_tree	1	100	0.894	0.005145	0.000427	0.917	7.627563	7.653782	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	022f7445d43bb1dbc24dc3106c03cb93	edf7cb014c2c0910e1c3aaaf8ae633f2	0.042581	0.005913	NaN	0.000376	0.000043	kd_tree	-1	100	0.894	0.006052	0.001592	0.917	7.035754	7.274997	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	84622ba45e941db642965553529e1941	edf7cb014c2c0910e1c3aaaf8ae633f2	0.022603	0.000676	NaN	0.000708	0.000023	kd_tree	1	5	0.911	0.000728	0.000051	0.905	31.031421	31.106905	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	a9455565db1d8e052a783317c99744ff	edf7cb014c2c0910e1c3aaaf8ae633f2	0.020226	0.000805	NaN	0.000791	0.000020	kd_tree	1	1	0.891	0.000423	0.000029	0.879	47.792957	47.905365	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.982	0.061	0.027	0.0	-1	1	0.782	0.060	3.813	3.824	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.731	0.078	0.021	0.0	-1	5	0.768	0.016	4.856	4.857	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.876	0.067	0.021	0.0	1	100	0.765	0.029	5.066	5.070	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.019	0.131	0.020	0.0	-1	100	0.736	0.012	5.462	5.463	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.941	0.122	0.020	0.0	1	5	0.742	0.053	5.309	5.323	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.780	0.108	0.021	0.0	1	1	0.727	0.015	5.196	5.197	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.018	0.0	-1	1	0.005	0.005	0.170	0.240	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	-1	5	0.001	0.001	0.411	0.487	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	100	0.001	0.000	0.642	0.647	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	-1	100	0.001	0.000	0.664	0.669	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	1	5	0.001	0.000	0.568	0.578	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.024	0.0	1	1	0.001	0.000	0.764	0.769	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.851	1.064	0.000	0.001	-1	1	0.102	0.003	8.329	8.334	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	9.508	9.987	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.080	0.386	0.000	0.001	-1	5	0.203	0.013	5.326	5.337	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.530	7.739	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.637	0.624	0.000	0.006	1	100	0.575	0.011	9.802	9.804	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	1	100	0.001	0.000	4.430	4.535	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.260	0.234	0.000	0.003	-1	100	0.560	0.004	5.819	5.819	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.002	0.000	0.005	-1	100	0.001	0.000	6.466	6.660	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.596	0.171	0.000	0.002	1	5	0.191	0.004	8.335	8.337	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.418	3.825	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.939	0.361	0.000	0.001	1	1	0.105	0.002	8.942	8.943	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.451	3.603	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.029	0.016	0.001	0.000	-1	1	0.001	0.000	58.057	60.198	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	21.601	23.371	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.001	0.001	0.000	-1	5	0.001	0.000	33.651	33.772	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	18.448	20.076	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.039	0.011	0.000	0.000	1	100	0.005	0.000	7.628	7.654	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.071	5.541	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.043	0.006	0.000	0.000	-1	100	0.006	0.002	7.036	7.275	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	17.727	19.093	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.001	0.001	0.000	1	5	0.001	0.000	31.031	31.107	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.148	5.406	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.001	0.001	0.000	1	1	0.000	0.000	47.793	47.905	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.400	5.878	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.587	0.083	30	0.027	0.0	random	0.444	0.027	1.320	1.323	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.659	0.025	30	0.024	0.0	k-means++	0.486	0.035	1.358	1.362	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.899	0.224	30	0.136	0.0	random	2.799	0.117	2.108	2.110	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.141	0.055	30	0.130	0.0	k-means++	2.898	0.021	2.119	2.119	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.009	0.000	random	0.0	0.0	8.040	13.403	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	0.0	0.0	8.637	12.765	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.009	0.000	k-means++	0.0	0.0	13.116	14.579	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	13.739	14.325	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.417	0.000	random	0.0	0.0	6.912	7.507	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	random	0.0	0.0	12.761	13.305	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.386	0.000	k-means++	0.0	0.0	7.271	8.201	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	13.761	14.274	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 prediction (50.0%). The observed differences can be found in the diff_adjusted_rand_scores column. The chosen difference threshold is 0.001 for adjusted_rand_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	3db9c030f488d0e8ff350ae449da4627	058b7e3842b587a5c675518f0706f5ee	0.001948	0.000233	20	0.008213	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000453	0.000031	-0.000965	4.303341	4.313745	0.001090
4	KMeans_short	predict	10000	1000	2	ac9b8cec4749455c0d1cd1a92c150716	058b7e3842b587a5c675518f0706f5ee	0.002082	0.000137	20	0.007685	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000481	0.000091	-0.000750	4.330115	4.406515	0.001995
7	KMeans_short	predict	10000	1000	100	3db9c030f488d0e8ff350ae449da4627	7f85b913f395ec54101a6738ea63a9a7	0.002702	0.000182	20	0.296097	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.000977	0.000133	0.293767	2.765744	2.791396	0.015034
10	KMeans_short	predict	10000	1000	100	ac9b8cec4749455c0d1cd1a92c150716	7f85b913f395ec54101a6738ea63a9a7	0.002908	0.000264	20	0.275116	0.000003	full	k-means++	20	300	1	1.000000e-16	0.317011	0.001113	0.000144	0.256968	2.613108	2.634997	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.087	0.003	20	0.002	0.0	random	0.030	0.002	2.925	2.930	See	See
3	KMeans_short	fit	10000	10000	2	0.251	0.008	20	0.001	0.0	k-means++	0.093	0.002	2.693	2.694	See	See
6	KMeans_short	fit	10000	10000	100	0.223	0.007	20	0.036	0.0	random	0.125	0.005	1.779	1.781	See	See
9	KMeans_short	fit	10000	10000	100	0.663	0.016	20	0.012	0.0	k-means++	0.347	0.005	1.910	1.911	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	random	0.000	0.0	4.303	4.314	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	12.794	13.069	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	k-means++	0.000	0.0	4.330	4.407	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	13.185	13.722	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.296	0.000	random	0.001	0.0	2.766	2.791	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.001	0.002	random	0.000	0.0	10.439	10.594	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.275	0.000	k-means++	0.001	0.0	2.613	2.635	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	10.662	10.861	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	f11be2529b0a77bc8fb5ee8de141e6b2	09c36ed6521e03974d0c134895f56c01	0.000429	0.000451	[20]	1.862965	4.294230e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000773	0.001263	0.55	0.555826	1.064991	0.01
4	LogisticRegression	predict	1000	100	10000	f11be2529b0a77bc8fb5ee8de141e6b2	7daecaea01e5baa8c862f264e3c1c3b0	0.001742	0.000170	[26]	4.593365	1.741643e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.003393	0.000188	0.28	0.513280	0.514067	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.369	0.511	[20]	0.070	0.000	1.939	0.050	5.863	5.865	See	See
3	LogisticRegression	fit	1000	1000	10000	0.970	0.660	[26]	0.082	0.001	0.722	0.049	1.344	1.347	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	1.863	0.0	0.001	0.001	0.556	1.065	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.015	0.0	0.000	0.000	0.384	0.392	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.593	0.0	0.003	0.000	0.513	0.514	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.002	0.0	0.001	0.000	0.118	0.118	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). The observed differences can be found in the diff_r2_scores column. The chosen difference threshold is 0.001 for r2_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	diff_r2_scores
1	Ridge	predict	1000	1000	10000	a70ae8dc5c059ec88cf27c61016aee2a	14fae294d530397144c65ba3209bf125	0.0099	0.000205	NaN	8.081125	0.00001	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.016407	0.000349	0.122191	0.603372	0.603509	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.186	0.004	0.431	0.0	0.198	0.015	0.937	0.940	See	See
3	Ridge	fit	1000000	1000000	100	1.195	0.107	0.669	0.0	0.323	0.267	3.701	4.805	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.000	8.081	0.0	0.016	0.0	0.603	0.604	See	See
2	Ridge	predict	1000	1	10000	0.00	0.001	0.258	0.0	0.000	0.0	3.158	3.381	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.000	5.036	0.0	0.000	0.0	0.488	0.698	See	See
5	Ridge	predict	1000000	1	100	0.00	0.000	0.013	0.0	0.000	0.0	0.742	0.782	See	See